Implementing Real-Time Anomaly Detection in Mobile Security Logs Using Artificial Intelligence

With the increasing reliance on mobile applications, security threats such as unauthorized access, malware attacks, and network intrusions have become more sophisticated. Traditional rule-based security mechanisms often fail to detect novel and evolving threats, necessitating the integration of Artificial Intelligence (AI) for real-time anomaly detection. This study proposes an AI-driven framework for identifying anomalies in mobile security logs using machine learning (ML) and deep learning (DL) models. The research employs Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks to analyze security log patterns and detect malicious behavior dynamically. The results demonstrate that the LSTM-based model outperforms other approaches, achieving 96.8% accuracy with a low false positive rate (3.1%), making it the most effective model for real-time threat detection. The system is integrated with a Flask-based API, enabling mobile applications to transmit logs for continuous monitoring and automated anomaly detection. Despite its effectiveness, the LSTM model requires optimization for real-time processing, and future work will focus on reducing computational overhead through model compression techniques. This study contributes to the advancement of AI-based mobile cybersecurity, providing an adaptive and scalable solution for protecting mobile users against emerging threats.